Energy trading framework for electric vehicles: an assignment matching‐theoretic game

Abstract

Electric Vehicles (EVs) can be considered as a flexible source of energy which can receive some benefit in terms of incentives for selling their energy. For efficient and economic trading amongst the EV owners, various researchers have proposed a variety of preference based matching algorithms like College Admission Framework (CAF), Max-weight, Merge and Split, Gale-Shapley Algorithm and Brute-Force Algorithm etc, where buyer and seller EVs can exchange energy and receive better payoffs. Unlike the above mentioned algorithms, in this paper the participating entities do not submit the preferences menu (which contains preferred choices of sellers (of a buyer) and of buyers (for a seller)) to a central authority.) However, this paper proposes an assignment energy trading game where no central authority is needed, the matching algorithm is hosted on cloud which matches charging and discharging of EVs based on their aspiration level and bids. The contribution of the work is to deduce the bids and aspiration level of charging and discharging EVs which is not considered in any of the existing work. Another contribution of the work is the behavioral assignment game that eludes the need of integer linear programming problem and deduces the convergence of game by adjustments of aspiration levels. Futhermore, the entire algorithm is cloud hosted with no middleman hence trading EVs identities are concealed from each other making the system unbiased. The proposed game helps both the buyer and the seller side of EVs to achieve their best bids as well as by reducing grid dependency it boosts the profit margin of the charging stations (CS).